Developing an Intelligent Model for Detecting Financial Fraud in Corporate Financial Statements Using Deep Neural Networks and Data Mining Techniques
Keywords:
Financial fraud, Financial statements, Deep neural networks, Data mining, Intelligent auditing, Thematic analysis, Anomaly detection.Abstract
Objective: This study aimed to develop an intelligent model for detecting financial statement fraud in companies using deep neural networks and data mining techniques based on the perceptions and experiences of financial, auditing, and data analytics experts. This qualitative study was conducted using thematic analysis. The participants included independent auditors, chief financial officers, data mining specialists, capital market analysts, and internal control experts based in Tehran. Purposeful and snowball sampling strategies were used, and data were collected through semi-structured interviews. The interview process continued until theoretical saturation was achieved, and 21 participants were finally included. All interviews were recorded, transcribed verbatim, and analyzed using NVivo software. Data analysis followed the six-phase thematic analysis procedure, including familiarization with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the final report. Trustworthiness was enhanced through member checking, peer debriefing, maintaining an audit trail, and continuous comparison of codes and categories. The analysis resulted in five main categories: data-driven architecture for fraud detection, financial and non-financial red flags, deep neural network and data mining design, interpretability and model governance, and organizational and auditing implementation requirements. The findings indicated that intelligent fraud detection cannot rely solely on traditional financial ratios. Instead, it requires the integration of structured financial data, textual disclosures, management discussion reports, audit evidence, behavioral indicators, and corporate governance variables. Participants emphasized that deep neural networks, especially when combined with feature selection, anomaly detection, and natural language processing, can reveal hidden patterns of financial manipulation. However, they also stressed that model outputs must be interpretable and usable for auditors, regulators, and financial decision-makers. The proposed model conceptualizes financial statement fraud detection as a multilayered, data-driven, interpretable, and human–AI collaborative process. It can support internal auditing, external auditing, capital market supervision, and corporate risk management by enabling preventive decision-making, improving financial transparency, and reducing information asymmetry.
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